Transient and persistent representation of a unified table metadata graph

ABSTRACT

Loading of table metadata into memory of an in-memory database is initiated. The table metadata is persisted across pages in a page chain. Thereafter, a plurality of metadata objects are materialized into memory that each include an object handle pinning an underlying persisted page in the page chain. The objects are populated with data from the underlying persisted pages. Subsequently, for one to many object relationships, a vector of object handles is generated that comprises a plurality of transient handles that each point to a different instance of a respective transient object. Alternatively, for one to one object relationships or many to one object relationships, an object handle to point to a respective linked object is generated. Related apparatus, systems, techniques and articles are also described.

TECHNICAL FIELD

The subject matter described herein relates a unified table metadatagraph being represented both transiently and as persisted.

BACKGROUND

In-memory databases are database management systems in which data isprimarily stored transiently; namely in main memory. In order to obtainoptimum performance, as much data as possible must be kept in memory.However, with very large tables, it is not always possible to load theentire table into memory. Therefore, larger tables can be loaded intomemory from persistence only partially, by loading and unloading partsof the table.

SUMMARY

In one aspect, loading of table metadata into memory of an in-memorydatabase is initiated. The table metadata is persisted across pages in apage chain. Thereafter, a plurality of metadata objects are materializedinto memory that each include an object handle pinning an underlyingpersisted page in the page chain. The objects are populated with datafrom the underlying persisted pages. Subsequently, for one to manyobject relationships, a vector of object handles is generated thatcomprises a plurality of transient handles that each point to adifferent instance of a respective transient object. Alternatively, forone to one object relationships or many to one object relationships, anobject handle to point to a respective linked object is generated.

The metadata objects can include table fragments, table metadata,dictionary objects, columns, column fragments, the page chain, andnumerous other types of objects. Modifications to the table metadata canbe performed solely in the page chain.

A first page in the page chain can include a table descriptorcharacterizing the metadata tree stored within the page chain. Themetadata tree can be formed within the page chain using links whichspecify a portion of the associated page that corresponds to the objectpointed to by one of the links or transient object handles. The tabledescriptor can form a root of a tree represented by links betweenobjects, forming a linked chain sequentially linking each descriptor for1:n relationships or storing a single link for 1:0 . . . 1 or n:1relationships.

The table descriptor can form a linked chain sequentially linking eachdescriptor. Each page in the page chain can be linked. The in-memorydatabase can be a column-oriented database that stores data tables assections of columns. The table metadata can be used to generate aunified table.

Non-transitory computer program products (i.e., physically embodiedcomputer program products) are also described that store instructions,which when executed by one or more data processors of one or morecomputing systems, causes at least one data processor to performoperations herein. Similarly, computer systems are also described thatmay include one or more data processors and memory coupled to the one ormore data processors. The memory may temporarily or permanently storeinstructions that cause at least one processor to perform one or more ofthe operations described herein. In addition, methods can be implementedby one or more data processors either within a single computing systemor distributed among two or more computing systems. Such computingsystems can be connected and can exchange data and/or commands or otherinstructions or the like via one or more connections, including but notlimited to a connection over a network (e.g. the Internet, a wirelesswide area network, a local area network, a wide area network, a wirednetwork, or the like), via a direct connection between one or more ofthe multiple computing systems, etc.

The subject matter described herein provides many technical advantages.For example, the current subject matter allows for objects to beefficiently represented both in a transient state (i.e., loaded intomemory) and in a persistent state (i.e., stored to a physical storage).

The details of one or more variations of the subject matter describedherein are set forth in the accompanying drawings and the descriptionbelow. Other features and advantages of the subject matter describedherein will be apparent from the description and drawings, and from theclaims.

DESCRIPTION OF DRAWINGS

FIG. 1 is a diagram illustrating features of a business software systemarchitecture;

FIG. 2 is another diagram illustrating features of a business softwaresystem architecture;

FIG. 3 is a schematic representation of fragments stored in a mainstore;

FIG. 4 is a diagram illustrating features of a unified table containerpage chain;

FIG. 5 is a diagram illustrating features of a unified table delta;

FIG. 6 is a diagram illustrating features of a unified table unsorteddictionary;

FIG. 7 is a functional block diagram illustrating performing a deltamerge operation and a read operation using a unified table;

FIG. 8 is a diagram illustrating twin representation in a transientstate and in a persistent state; and

FIG. 9 is a process flow diagram illustrating assembly of at least aportion of a persisted table in memory.

Like reference symbols in the various drawings indicate like elements.

DETAILED DESCRIPTION

The current subject matter includes a number of aspects that can beapplied individually or in combinations of one or more such aspects tosupport a unified database table approach that integrates theperformance advantages of in-memory database approaches with the reducedstorage costs of on-disk database approaches. The current subject mattercan be implemented in database systems using in-memory OLAP, for exampleincluding databases sized at several terabytes (or more), tables withbillions (or more) of rows, and the like; systems using in-memory OLTP(e.g. enterprise resource planning or ERP system or the like, forexample in databases sized at several terabytes (or more) with hightransactional volumes; and systems using on-disk OLAP (e.g. “big data,”analytics servers for advanced analytics, data warehousing, businessintelligence environments, or the like), for example databases sized atseveral petabytes or even more, tables with up to trillions of rows, andthe like.

The current subject matter can be implemented as a core softwareplatform of an enterprise resource planning (ERP) system, other businesssoftware architecture, or other data-intensive computing application orsoftware architecture that runs on one or more processors that are underthe control of a specific organization. This arrangement can be veryeffective for a large-scale organization that has very sophisticatedin-house information technology (IT) staff and for whom a sizablecapital investment in computing hardware and consulting servicesrequired to customize a commercially available business softwaresolution to work with organization-specific business processes andfunctions is feasible. FIG. 1 shows a diagram 100 of a system consistentwith such an implementation. A computing system 110 can include one ormore core software platform modules 120 providing one or more featuresof the business software system. The computing system can also aggregateor otherwise provide a gateway via which users can access functionalityprovided by one or more external software components 130. Clientmachines 140 can access the computing system, either via a directconnection, a local terminal, or over a network 150 (e.g. a local areanetwork, a wide area network, a wireless network, the Internet, or thelike).

A database management agent 160 or other comparable functionality canaccess a database management system 170 that stores and provides accessto data (e.g. definitions of business scenarios, business processes, andone or more business configurations as well as data, metadata, masterdata, etc. relating to definitions of the business scenarios, businessprocesses, and one or more business configurations, and/or concreteinstances of data objects and/or business objects that are relevant to aspecific instance of a business scenario or a business process, and thelike. The database management system 170 can include at least one table180 and additionally include parallelization features consistent withthose described herein.

FIG. 2 shows a block diagram of an architecture 200 illustratingfeatures that can be included in a database or database managementsystem consistent with implementations of the current subject matter. Atable data store 202, which can be retained among a plurality of datavolumes 204, can include one or more of a delta store 206 (e.g. a pageddelta part, which can optionally be OLTP optimized and can optionallyinclude a merge process 208), an index store 212 (e.g. one or moresegmented indices), and a main store 210. The main store 210 can includea main part that is fragmented consistent with features describedherein.

To achieve a best possible compression and also to support very largedata tables, a main part of the table can be divided into one or morefragments. FIG. 3 shows a schematic representation 300 of the variousfragments stored in main store 210. One or more main fragments orfragments 330 can be used for each table or column of a database. Small,manageable tables can be represented with a single fragment. Very largetables can be split into two or more table partitions 335. Each tablepartition may, in turn, include two or more fragments 330. Fragments 330can be horizontal slices of the table to which they belong. Eachfragment 330 can include one or more column fragments 340. Each columnfragment 340 can have its own dictionary and value ID array consistentwith the features described herein.

Fragments 330 can advantageously be sufficiently large to gain maximumperformance due to optimized compression of the fragment and highin-memory performance of aggregations and scans. Conversely, suchfragments can be sufficiently small to load a largest column of anygiven fragment into memory and to sort the fragment in-memory. Fragmentscan also be sufficiently small to be able to coalesce two or morepartially empty fragments into a smaller number of fragments. As anillustrative and non-limiting example of this aspect, a fragment cancontain one billion rows with a maximum of 100 GB of data per column.Other fragment sizes are also within the scope of the current subjectmatter. A fragment can optionally include a chain of pages. In someimplementations, a column can also include a chain of pages. Column datacan be compressed, for example using a dictionary and/or any othercompression method. Table fragments can be materialized in-memory incontiguous address spaces for maximum performance. All fragments of thedatabase can be stored on-disk, and access to these fragments can bemade based on an analysis of the data access requirement of a query.

Referring again to FIG. 2, other parts of the architecture 200 caninclude a data manipulation language (DML) handling module or similarfunctionality 214, one or more query handling modules or similarfunctionality 216 (e.g. including multi-version concurrency control), anindex builder 220 that supports the index store 212, a query languageengine 222 (which can, for example, be a SQL engine), a complex eventsprocessing module (e.g. an event handler, a stream processing module,etc.) 224 for receiving inputs from a user 226, and the like.

FIG. 4 shows a block diagram illustrating an example of a unified tablecontainer page chain 400. As described above, each fragment canoptionally include a chain of pages. In general, a container can berepresented as a page chain. A page chain can generally be characterizedas a set of pages that are linked in a given order. The term pages, asused herein, refers to a basic unit of storage in a database. A pagesize is generally established when the database is built and typicallycannot be changed. A representative page size can be on the order of 2kB, 4 kB, 8 kB, 16 kB, or the like. Once the server is built, the valueusually cannot be changed. Different types of pages can store differenttypes of database objects. For example, data pages can store data rowsor columns for a table. Index pages can store index rows for one or morelevels of an index. Large object (LOB) pages can store data for text andimage columns, for Java off-row columns, and the like.

Also as shown in FIG. 4, sub-chains of the page chain can be defined fora delta part, a main part, dictionaries, index segments (optionally, notshown in FIG. 2), and the like such that a “whole” of each of theseentities contains one or more pages. In some implementations of thecurrent subject matter, a delta part can include both “hot” deltafragments 402 and “cold” delta fragments 404, which can be storedseparately. The main part can also be subdivided into main fragments330. Pages containing dictionary-compressed columnar data 410 can referto pages containing dictionaries for them. Individual table parts can beloaded into main memory on-demand. A merge process can be decoupled fromtransaction handling such that a merge process can be executed atrecovery time (e.g. during log replay). A page chain, such as theexample shown in FIG. 4, can be initiated by a container directory entry(CDE) 412.

A single RowID space can be used across pages in a page chain. A RowID,which generally refers to a logical row in the database, can be used torefer to a logical row in an in-memory portion of the database and alsoto a physical row in an on-disk portion of the database. A row indextypically refers to physical 0-based index of rows in the table. A0-based index can be used to physically address rows in a contiguousarray, where logical RowIDs represent logical order, not physicallocation of the rows. In some in-memory database systems, a physicalidentifier for a data record position can be referred to as a UDIV orDocID. Distinct from a logical RowID, the UDIV or DocID (or a comparableparameter) can indicate a physical position of a row (e.g. a datarecord), whereas the RowID indicates a logical position. To allow apartition of a table to have a single RowID and row index spaceconsistent with implementations of the current subject matter, a RowIDcan be assigned a monotonically increasing ID for newly-inserted recordsand for new versions of updated records across fragments. In otherwords, updating a record will change its RowID, for example, because anupdate is effectively a deletion of an old record (having a RowID) andinsertion of a new record (having a new RowID). Using this approach, adelta store of a table can be sorted by RowID, which can be used foroptimizations of access paths. Separate physical table entities can bestored per partition, and these separate physical table entities can bejoined on a query level into a logical table.

When an optimized compression is performed during a columnar mergeoperation to add changes recorded in the delta store to the main store,the rows in the table are generally re-sorted. In other words, the rowsafter a merge operation are typically no longer ordered by theirphysical row ID. Therefore, stable row identifier can be used consistentwith one or more implementations of the current subject matter. Thestable row identifiers can optionally be a logical RowID. Use of astable, logical (as opposed to physical) RowID can allow rows to beaddressed in REDO/UNDO entries in a write-ahead log and transaction undolog. Additionally, cursors that are stable across merges without holdingreferences to the old main version of the database can be facilitated inthis manner. To enable these features, a mapping of an in-memory logicalRowID to a physical row index and vice versa can be stored. In someimplementations of the current subject matter, a RowID column can beadded to each table. The RowID column can also be amenable to beingcompressed in some implementations of the current subject matter.

FIG. 5 shows a block diagram of a unified table delta 500 consistentwith one or more implementations of the current subject matter. In someexamples, a “hot” and “cold” delta approach can be used in whichuncompressed data are retained in the “hot” delta part, whiledictionary-compressed data are retained in the “cold” delta part with amini-merge performed between the hot and cold parts. Such a delta partcan be considered as a single container. As shown in FIG. 5, each deltasub-chain can have its own transient structure. In other words, aseparate structure can be used for each delta. A page vector 502 canhold page handles to individual pages 504 and can allow a fast iterationover the pages 504 (for example as part of a column or table scan). Apage handle to an individual page 504 can include a pin or the like heldin memory. As used herein, the term “pin” refers to holding a particulardata page (which may also have been stored on disk) in memory. As anexample, if a page is not pinned, it can be cleared from memory. Pinningis typically done on data pages being actively accessed so as to avoidpotential performance degradations associated with reading the page fromdisk into memory.

A RowID index 506 can serve as a search structure to allow a page 504 tobe found based on a given interval of RowID values. The search time canbe on the order of log n, where n is very small. The RowID index canprovide fast access to data via RowID values. For optimization, “new”pages can have a 1:1 association between RowID and row index, so thatsimple math (no lookup) operations are possible. Only pages that arereorganized by a merge process need a RowID index in at least someimplementations of the current subject matter.

FIG. 6 shows a block diagram of a unified table unsorted dictionary 600.Consistent with one or more implementations of the current subjectmatter, column data in a delta part can use unsorted dictionaries. Atransient structure can be provided per delta column dictionary. Thepage vector 502 can handle pinning of pages in memory. Direct access canbe provided via a pointer from other structures. A value vectorindirection 602 can allow a same number of values per dictionary block604. This capability can support an order of 1 performance cost forlookup of a value by ValuelD. A dictionary can assign a unique ValuelD(typically a numeric value) to each unique value such that the uniquevalues (which are typically larger in memory size than the ValuelD) canbe stored once rather than multiple times. A value array is a structureused by the dictionary to retrieve values given a ValuelD or vice versa.This technique, which can reduce the amount of memory needed to store aset of values where the values are not unique, is typically referred toas dictionary compression. A Value to ValuelD map 606 can support hashor B-tree sizes on the order of 1 or on the order of log n for lookup ofValuelD by value. A B-tree is a tree data structure that keeps datasorted and allows searches, sequential access, insertions, and deletionsin logarithmic time. This capability can be necessary for dictionarycompression. A B-tree can be better for range scans but can be moreexpensive to maintain.

FIG. 7 shows a functional block diagram 700 for performing a delta mergeoperation 710 on a unified table. New transactions or changes caninitially be written into delta store 206. Main store 210 can includeone active fragment 712 and one or more closed fragments 716. Whenupdates are merged from delta store 206 into the main store 210,existing records in the closed fragments 716 cannot be changed. Instead,new versions of the records can be added to the active fragment 712, andold versions can be marked as invalid.

Functional block diagram 700 also illustrates a read operation 720.Generally, read operations can have access to all fragments (i.e.,active fragment 712 and closed fragments 716). Read operations can beoptimized by loading only the fragments that contain data from aparticular query. Fragments that do not contain such data can beexcluded. In order to make this decision, container-level metadata(e.g., a minimum value and/or a maximum value) can be stored for eachfragment. This metadata can be compared to the query to determinewhether a fragment contains the requested data.

With reference to diagram 800 of FIG. 8, a table 810 can be loaded intothe memory of the database 170. The table 810 can include a fragmentvector 820 that is an array of transient handles (e.g., pointers,references, etc.) that can each refer to a different fragment 830_(1 . . . n). The table 810 can also include a transient handle (e.g.,pointer, reference, etc.) to a first page 840 ₁ that forms part of apage chain 830 _(1 . . . n) (similar to page chain 400 of FIG. 4). Thefirst page 840 ₁ can include a persistent table descriptor 850 which isthe root of a tree structure of the table, describing each column,fragment, page chain, and/or other structures of the table. The tree ofobjects describing the structure of the table is stored within the pagechain. Each fragment 830 _(1 . . . n) can include a corresponding objecthandle to its persistent descriptor 860 _(1 . . . n), that pins theunderlying pages 840 _(1 . . . m) in memory. Pinning in this regardmeans that the corresponding memory cannot be swapped out. Inparticular, the corresponding object handle can point to a persistentfragment descriptor 830 _(1 . . . n) that identifies which portion ofthe associated page 840 corresponds to such fragment 830. In some cases,there can be multiple fragments 830 per page 840.

It will be appreciated that with some variations, diagram 800 is asimplification as there can be many different objects at differentlevels of a hierarchy. On a first level, the fragments 830 can have a1:n relation to column fragments. The table 810 can have a 1:n relationto column descriptors (that characterize the column fragments, etc.).The column fragments can have an n:1 relation to the column descriptors.Other objects relating to the dictionary for the delta and the main alsocan have a twin transient/persistent representation. All persistentmetadata descriptors in the metadata graph have their respectivetransient object pointing to it via an object handle (and thus pinningthem in memory).

FIG. 9 is a diagram 900 in which, at 910, loading of at least a portionof table metadata into memory of an in-memory database is initiated. Thetable metadata is persisted across pages in a page chain. Thereafter, at920, a plurality of metadata objects are materialized into memory thateach comprise an object handle pinning an underlying persisted page inthe page chain. For 1:n relationships, such as table root object totable fragment, table to column metadata or fragment to column fragment,a vector of object handles is then generated, at 930, that comprises aplurality of transient handles that each point to a different instanceof transient linked object of the respective type. As an example, FIG. 8shows a table-to-fragment relationship. A fragment vector is generatedin a transient table object, which contains transient handles toindividual fragments. Each transient object (such as table and fragmentin this example) holds a persistent object handle to the underlyingpersistent object (pinning it in memory). For 1:0 . . . 1 and n:1relationships, such as column fragment to column metadata, columnfragment to dictionary, column fragment to its page chain or(first/delta) fragment to (PAX data) page chain, instead of a vector, at940, a simple object handle is generated to point to the respectivelinked object.

One or more aspects or features of the subject matter described hereincan be realized in digital electronic circuitry, integrated circuitry,specially designed application specific integrated circuits (ASICs),field programmable gate arrays (FPGAs) computer hardware, firmware,software, and/or combinations thereof. These various aspects or featurescan include implementation in one or more computer programs that areexecutable and/or interpretable on a programmable system including atleast one programmable processor, which can be special or generalpurpose, coupled to receive data and instructions from, and to transmitdata and instructions to, a storage system, at least one input device,and at least one output device. The programmable system or computingsystem may include clients and servers. A client and server aregenerally remote from each other and typically interact through acommunication network. The relationship of client and server arises byvirtue of computer programs running on the respective computers andhaving a client-server relationship to each other.

These computer programs, which can also be referred to as programs,software, software applications, applications, components, or code,include machine instructions for a programmable processor, and can beimplemented in a high-level procedural language, an object-orientedprogramming language, a functional programming language, a logicalprogramming language, and/or in assembly/machine language. As usedherein, the term “machine-readable medium” refers to any computerprogram product, apparatus and/or device, such as for example magneticdiscs, optical disks, memory, and Programmable Logic Devices (PLDs),used to provide machine instructions and/or data to a programmableprocessor, including a machine-readable medium that receives machineinstructions as a machine-readable signal. The term “machine-readablesignal” refers to any signal used to provide machine instructions and/ordata to a programmable processor. The machine-readable medium can storesuch machine instructions non-transitorily, such as for example as woulda non-transient solid-state memory or a magnetic hard drive or anyequivalent storage medium. The machine-readable medium can alternativelyor additionally store such machine instructions in a transient manner,such as for example as would a processor cache or other random accessmemory associated with one or more physical processor cores.

To provide for interaction with a user, one or more aspects or featuresof the subject matter described herein can be implemented on a computerhaving a display device, such as for example a cathode ray tube (CRT) ora liquid crystal display (LCD) or a light emitting diode (LED) monitorfor displaying information to the user and a keyboard and a pointingdevice, such as for example a mouse or a trackball, by which the usermay provide input to the computer. Other kinds of devices can be used toprovide for interaction with a user as well. For example, feedbackprovided to the user can be any form of sensory feedback, such as forexample visual feedback, auditory feedback, or tactile feedback; andinput from the user may be received in any form, including, but notlimited to, acoustic, speech, or tactile input. Other possible inputdevices include, but are not limited to, touch screens or othertouch-sensitive devices such as single or multi-point resistive orcapacitive trackpads, voice recognition hardware and software, opticalscanners, optical pointers, digital image capture devices and associatedinterpretation software, and the like.

In the descriptions above and in the claims, phrases such as “at leastone of” or “one or more of” may occur followed by a conjunctive list ofelements or features. The term “and/or” may also occur in a list of twoor more elements or features. Unless otherwise implicitly or explicitlycontradicted by the context in which it is used, such a phrase isintended to mean any of the listed elements or features individually orany of the recited elements or features in combination with any of theother recited elements or features. For example, the phrases “at leastone of A and B;” “one or more of A and B;” and “A and/or B” are eachintended to mean “A alone, B alone, or A and B together.” A similarinterpretation is also intended for lists including three or more items.For example, the phrases “at least one of A, B, and C;” “one or more ofA, B, and C;” and “A, B, and/or C” are each intended to mean “A alone, Balone, C alone, A and B together, A and C together, B and C together, orA and B and C together.” In addition, use of the term “based on,” aboveand in the claims is intended to mean, “based at least in part on,” suchthat an unrecited feature or element is also permissible.

The subject matter described herein can be embodied in systems,apparatus, methods, and/or articles depending on the desiredconfiguration. The implementations set forth in the foregoingdescription do not represent all implementations consistent with thesubject matter described herein. Instead, they are merely some examplesconsistent with aspects related to the described subject matter.Although a few variations have been described in detail above, othermodifications or additions are possible. In particular, further featuresand/or variations can be provided in addition to those set forth herein.For example, the implementations described above can be directed tovarious combinations and subcombinations of the disclosed featuresand/or combinations and subcombinations of several further featuresdisclosed above. In addition, the logic flows depicted in theaccompanying figures and/or described herein do not necessarily requirethe particular order shown, or sequential order, to achieve desirableresults. Other implementations may be within the scope of the followingclaims.

What is claimed is:
 1. A method comprising: initiating loading of tablemetadata into memory of an in-memory database, the table metadata beingpersisted across a plurality of pages in a page chain, wherein a firstpage in the page chain comprises a table descriptor characterizing ametadata tree stored within the page chain; materializing a plurality ofmetadata objects into memory, each metadata object of the plurality ofmetadata objects comprising an object handle pinning an underlyingpersisted page in the page chain to hold the underlying persisted pagein the memory; generating, for one to many object relationships, avector of object handles that comprises a plurality of transienthandles, each transient handle of the plurality of transient handlespointing to a different instance of a respective transient object;generating, for one to one object relationships and many to one objectrelationships, an associated object handle to point to a respectivelinked object; and performing, in response to receiving a databasequery, a read operation comprising comparing the plurality of metadataobjects to the database query to identify one or more of the pluralityof metadata objects that contain requested data in the database queryand loading the identified one or more of the plurality of metadataobjects that contain the requested data.
 2. The method of claim 1,wherein the metadata objects of the plurality of metadata objects areselected from a group consisting of: table fragments, table metadata,dictionary objects, columns, column fragments, and the page chain. 3.The method of claim 1, wherein modifications to the table metadata areperformed solely in the page chain.
 4. The method of claim 1, whereinthe metadata tree is formed within the page chain using links whichspecify a portion of the associated page that corresponds to the objectpointed to by one of the links or transient object handles.
 5. Themethod of claim 4, wherein the table descriptor forms a root of a treerepresented by links between objects, forming a linked chainsequentially linking each descriptor for 1:n relationships or storing asingle link for 1:0 . . . 1 or n:1 relationships.
 6. The method of claim1, wherein each page in the page chain is linked.
 7. The method of claim1, wherein the in-memory database is a column-oriented database thatstores data tables as sections of columns.
 8. The method of claim 1,wherein the table metadata describes a relational table.
 9. The methodof claim 1, wherein the initializing, materializing, populating andgenerating are implemented by at least one hardware data processorforming part of at least one computing device.
 10. A non-transitorycomputer program product storing instructions which, when executed by atleast one hardware data processor forming part of at least one computingdevice, result in operations comprising: initiating loading of tablemetadata into memory of an in-memory database, the table metadata beingpersisted across a plurality of pages in a page chain, wherein a firstpage in the page chain comprises a table descriptor characterizing ametadata tree stored within the page chain; materializing a plurality ofmetadata objects into memory, each metadata object of the plurality ofmetadata objects comprising an object handle pinning an underlyingpersisted page in the page chain to hold the underlying persisted pagein the memory; generating, for one to many object relationships, avector of object handles that comprises a plurality of transienthandles, each transient handle of the plurality of transient handlespointing to a different instance of a respective transient object;generating, for one to one object relationships and many to one objectrelationships, an associated object handle to point to a respectivelinked object; and performing, in response to receiving a databasequery, a read operation comprising comparing the plurality of metadataobjects to the database query to identify one or more of the pluralityof metadata objects that contain requested data in the database queryand loading the identified one or more of the plurality of metadataobjects that contain the requested data.
 11. The non-transitory computerprogram product of claim 10, wherein the metadata objects of theplurality of metadata objects are selected from a group consisting of:columns, column fragments, table fragments, table metadata, dictionaryobjects, and the page chain.
 12. The non-transitory computer programproduct of claim 10, wherein modifications to the table metadata areperformed solely in the page chain.
 13. The non-transitory computerprogram product of claim 10, wherein the metadata tree is formed withinthe page chain using links which specify a portion of the associatedpage that corresponds to the object pointed to by one of the links ortransient object handles.
 14. The non-transitory computer programproduct of claim 13, wherein the table descriptor forms a root of a treerepresented by links between objects, forming a linked chainsequentially linking each descriptor for 1:n relationships or storing asingle link for 1:0 . . . 1 or n:1 relationships.
 15. The non-transitorycomputer program product of claim 10, wherein each page in the pagechain is linked.
 16. The non-transitory computer program product ofclaim 10, wherein the in-memory database is a column-oriented databasethat stores data tables as sections of columns.
 17. The non-transitorycomputer program product of claim 10, wherein the table metadata is togenerate a unified table.
 18. A system comprising: at least one hardwaredata processor; and memory storing instructions which, when executed bythe at least one hardware data processor, result in operationscomprising: initiating loading of table metadata into memory of anin-memory database, the table metadata being persisted across aplurality of pages in a page chain, wherein a first page in the pagechain comprises a table descriptor characterizing a metadata tree storedwithin the page chain; materializing a plurality of metadata objectsinto memory, each metadata object of the plurality of metadata objectscomprising an object handle pinning an underlying persisted page in thepage chain to hold the underlying persisted page in the memory;generating, for one to many object relationships, a vector of objecthandles that comprises a plurality of transient handles, each transienthandle of the plurality of transient handles pointing to a differentinstance of a respective transient object; generating, for one to oneobject relationships and many to one object relationships, an associatedobject handle to point to a respective linked object; and performing, inresponse to receiving a database query, a read operation comprisingcomparing the plurality of metadata objects to the database query toidentify one or more of the plurality of metadata objects that containrequested data in the database query and loading the identified one ormore of the plurality of metadata objects that contain the requesteddata.